ABSTRACT The accelerating expansion of data‐centric technologies is sharply increasing the energy burden of information storage, placing unprecedented pressure on the efficiency of magnetic switching. Conventional field‐driven reversal, once the foundation of magnetic memory, has become impractical in modern architectures due to its high energy cost ...
Mohammad H. Badarneh +2 more
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Combining curriculum learning and weakly supervised attention for enhanced thyroid nodule assessment in ultrasound imaging. [PDF]
Keatmanee C +6 more
europepmc +1 more source
Weakly supervised multiple-instance active learning for tooth-marked tongue recognition. [PDF]
Deng F, Li S, Yang Z, Zhou W.
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A method for spatial interpretation of weakly supervised deep learning models in computational pathology. [PDF]
Sharma A, Liu B, Rantalainen M.
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LTGS-Net: Local Temporal and Global Spatial Network for Weakly Supervised Video Anomaly Detection. [PDF]
Li M, Wang X, Wang H, Yang M.
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Generative AI for weakly supervised segmentation and downstream classification of brain tumors on MR images. [PDF]
Yoo JJ +8 more
europepmc +1 more source
Discovering subtypes with imaging signatures in the Motoric Cognitive Risk Syndrome Consortium using weakly supervised clustering. [PDF]
Nallapu BT +15 more
europepmc +1 more source
Weakly supervised learning with stochastic supervision and knowledge transfer.
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Salvage of Supervision in Weakly Supervised Object Detection and Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023Weakly supervised vision tasks, including detection and segmentation, have attracted much attention in the vision community recently. However, the lack of detailed and precise annotations in the weakly supervised case leads to a large accuracy gap between weakly- and fully-supervised methods.
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2021 IEEE International Conference on Multimedia and Expo (ICME), 2021Semantic segmentation is a fundamental vision problem which aims to divide an image into non-overlapped regions and then assign them with predefined object labels. Despite its latest development (especially deep neural network-based), semantic segmentation is still far from comprehensive understanding of the visual world around us.
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